IMF | SARTTAC

Target Audience

Officials responsible for compiling and using intensely national accounts statistics (NAS).

Qualifications

Participants should have a degree in economics, statistics, or the equivalent.

Course Description

This course, presented by the IMF’s Statistics Department, aims at providing a thorough understanding of concepts, sources of data, and compilation techniques for producing quarterly national accounts (QNA) statistics. The course is based on the IMF’s Quarterly National Accounts Manual and is oriented toward national accounts compilers from countries that are developing or planning to develop QNA. The course covers both theoretical and practical issues in the compilation of QNA.

It covers the following main topics:

1.    Scope and role of QNA;

2.    Data sources for compiling quarterly gross domestic product (GDP) estimates (mainly from production and expenditure approaches);

3.    Benchmarking techniques for combining quarterly NAS indicators with the annual estimates;

4.    Seasonal adjustment;

5.    Price and volume measures;

6.    Revision policy and dissemination practices; and

7.    Other specific QNA issues.

The course is delivered primarily through interactive lectures and workshops. Some small group discussions may occur.Course Objectives

Course Objectives

Upon completion of this course, participants should be able to:

1.    Describe QNAS, including their compilation, scope, role, international standards, and best practices.

2.    Describe data requirements and methods for compiling different quarterly NAS series—especially GDP and its valuation.

3.    Illustrate the relationship between QNA aggregates and other aggregates within the SNA.

4.    Develop a framework for compiling selected NAS series, including the collection and development of source data to implement statistical methods for deriving selected NAS aggregates. Gain practical experience addressing specific issues related to the compilation and use of quarterly data.

5.    Describe the analytical usefulness of quarterly economic data series for quarterly GDP, the latter’s potential analytical usefulness, and advanced techniques for assessing economic activity accurately.